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The invention pertains to machine vision and, more particularly, to methods for inspection of leads on semiconductor die packages (or lead frames).
At the heart of an integrated circuit is a semiconductor die. This is a wafer of semiconducting material (e.g., silicon) with hundreds of thousands or millions of electronic circuit components etched into its layers. To enhance processing speed and reduce power consumption, the dies are made as small as possible, e.g., less than a square-inch in area and several mils thick. To facilitate handling, the dies are glued into supporting frames, i.e., lead frames. In addition to providing stability, these frames have large conductive leads that can be soldered to other circuit components, e.g., on a printed circuit board. The leads are typically connected to corresponding pads on the die via a process called wire bonding, wherein a small conductive thread is bonded to each lead and its corresponding pad. Once a semiconductor die and its frame are assembled, they are typically packaged in a ceramic or plastic, forming an integrated circuit.
Inspection of the lead area of the semiconductor die packages is important in the semiconductor industry. Such inspection typically involves checking the leads on the package; both before and after the die is bonded to the package.
The most common defect in assembly is the deposit of unwanted adhesive on the leads. This is sometimes referred to as an AOL defect. Since the adhesive is conductive, it can effectively xe2x80x9cshort circuitxe2x80x9d the semiconductor die""s electronic functions.
The inspection of semiconductor packages for adhesive on leads has proven to be a vexing machine vision problem. This is a result of the complexity of the xe2x80x9cbackground,xe2x80x9d i.e., the lead pattern which must be inspected in order to find the defect. This is further complicated by the decreasing size, and increasing number, of leads, as well as by the limited resolution of the cameras typically used for inspection. In this regard, it will be appreciated that while there are a variety of lead configurations, there are two basic types: etched leads and flying/free leads. The former are rigid and are etched onto a substrate, while the latter are mechanically pressed but non-rigid.
The prior art suggests the use of a technique referred to golden template comparison (GTC) to inspect the package leads. GTC is a technique for locating objects by comparing a feature under scrutiny (to wit, a lead frame) to a good imagexe2x80x94or golden templatexe2x80x94that is stored in memory. The technique subtracts the good image from the test image and analyzes the difference to determine if the expected object (e.g., a defect) is present. For example, upon subtracting the image of a good lead frame from a defective one, the resulting xe2x80x9cdifferencexe2x80x9d image would reveal an adhesive blotch that could be flagged as a defect.
Before GTC inspections can be performed, the system must be xe2x80x9ctrainedxe2x80x9d so that the golden template can be stored in memory. To this end, the GTC training functions are employed to analyze several good samples of a scene to create a xe2x80x9cmeanxe2x80x9d image and xe2x80x9cstandard deviationxe2x80x9d image. The mean image is a statistical average of all the samples analyzed by the training functions. It defines what a typical good scene looks like. The standard deviation image defines those areas on the object where there is little variation from part to part, as well as those areas in which there is great variation from part to part. This latter image permits GTC""s runtime inspection functions to use less sensitivity in areas of greater expected variation, and more sensitivity in areas of less expected variation.
At runtime, a system employing GTC captures an image of a scene of interest. Where the position of that scene is different from the training position, the captured image is aligned, or registered, with the mean image. The intensities of the captured image are also normalized with those of the mean image to ensure that variations illumination do not adversely affect the comparison.
The GTC inspection functions then subtract the registered, normalized, captured image from the mean image to produce a difference image that contains all the variations between the two. That difference image is then compared with a xe2x80x9cthresholdxe2x80x9d image derived from the standard deviation image. This determines which pixels of the difference image are to be ignored and which should be analyzed as possible defects. The latter are subjected to morphology, to eliminate or accentuate pixel data patterns and to eliminate noise. An object recognition technique, such as connectivity analysis, can then be employed to classify the apparent defects.
Although GTC inspection tools have proven quite successful, they suffer some limitations. For example, except in unusual circumstances, GTC requires registrationxe2x80x94i.e., that the image under inspection be registered with the template image. GTC also uses a standard deviation image for thresholding, which can result in a loss of resolution near edges due to high resulting threshold values. GTC is, additionally, limited to applications where the images are repeatable: it cannot be used where image-to-image variation results form changes in size, shape, orientation and warping.
GTC is typically used to inspect only etched lead configurations, where it can be effectively used if the lead count is not high. Where that count is high, the frequency of etches results in a large area being effectively masked by the high standard deviation at the lead edges. GTC has not proven effective in inspections of flying/free configurations. Moreover, it is limited in that it requires excessive memory or processing time in instances where the package under inspection is rotated.
Blob analysis is also used to inspect etched lead configurations, as well as free-flying lead configurations. However, this analysis technique is only effective if the lead count is not high.
An object of this invention, therefore, is to provide improved methods for machine vision and, more particularly, improved methods for inspecting leads on semiconductor die packages or lead frames.
A further object is to provide such methods that can be used to identify defects such as adhesive blotches on those leads.
Yet another object is to provide such methods that can be used in inspecting the full range of die packages, including both etched lead packages and flying/free lead packages.
Yet still another object is to provide such methods that do not routinely necessitate alignment or registration of an image under inspection with a template image.
Still yet a further object of the invention is to provide such methods that do not require training.
Still other objects of the invention include providing such machine vision methods as can be readily implemented on existing machine vision processing equipment, and which can be implemented for rapid execution without excessive consumption of computational power.
The foregoing objects are among those achieved by the invention which provides, in one aspect, a machine vision method for inspecting leads on semiconductor die package, or lead frame. The method includes the steps of generating a first image of the lead frame (including, its leads and other structuresxe2x80x94together, referred to as the xe2x80x9clead framexe2x80x9d or xe2x80x9cbackgroundxe2x80x9d), generating a second image of the lead frame and any defects thereon (e.g., excessive adhesive), and subtracting the second image from the first image. The method is characterized in that the second image is generated such that subtraction of it from the first image emphasizes the defect with respect to the background.
In related aspects of the invention, the second step is characterized as generating the second image such that its subtraction from the first image increases a contrast between the defect and the background. That step is characterized, in still further aspects of the invention, as being one that results in defect-to-background contrast differences in the second image that are of opposite polarity from the defect-to-contrast differences in the first image.
In further aspects, the invention calls for generating a third image with the results of the subtraction, and for isolating the expected defects on that third image. Isolation can be performed, according to other aspects of the invention, by conventional machine vision segmentation techniques such as connectivity analysis, edge detection and/or tracking, and by thresholding. In the latter regard, a threshold imagexe2x80x94as opposed to one or two threshold valuesxe2x80x94can be generated by mapping image intensity values of the first or second image. That threshold image can, then, be subtracted from the third image (i.e, the difference image) to isolate further the expected defects.
Still further objects of the invention provide for normalizing the first and second images before subtracting them to generate the third image. In this aspect, the invention determines distributions of intensity values of each of the first and second images, applying mapping functions to one or both of them in order to match the tails of those distributions. The first and second images can also be registered prior to subtraction.
According to further aspects of the invention, the first and second images are generated by illuminating the lead frame with different respective light or emission sources. This includes, for example, illuminating it direct, on-axis lighting to generate the first image, and illuminating it with diffuse, off-access or grazing light to generate the second image.
Additional aspects of the invention provide methods incorporating various combinations of the foregoing aspects.